HIPAA (USA)  ·  GDPR (EU)  ·  PHIPA (Ontario)  ·  PIPEDA (Canada)  ·  EU AI Act (EU)  ·  NIST AI RMF  ·  FDA AI/ML  ·  SOC 2  ·  ISO/IEC 42001  ·  ISO/IEC 27001  ·  OECD AI Principles

Security Frameworks vs Privacy Laws

Choosing the right compliance approach in healthcare AI often means balancing two different but related domains: security frameworks and privacy laws. While both aim to protect sensitive health data, they operate at different levels. Privacy laws define what must be protected and why, while security frameworks guide how to protect it through specific controls, processes, and certifications.

For IT teams, understanding this distinction is crucial. Misinterpreting frameworks as laws—or assuming legal compliance automatically means strong security—can leave dangerous gaps in protection, especially when deploying AI in clinical or healthtech environments.

Updated: September 20, 2025 

Understanding Privacy Laws

Privacy laws create the legal boundaries for handling patient and health data:

1. HIPAA (USA)

    • Protects Protected Health Information (PHI).

    • Requires administrative, technical, and physical safeguards to protect the confidentiality, integrity, and availability of data.

    • Requires Business Associate Agreements (BAAs) when data is shared.

2. GDPR (EU)

  • Comprehensive privacy law covering the personal data of individuals in the EU/EEA.

  • Extraterritorial reach for organizations outside the EU that target or monitor those individuals.

  • Focuses on data subject rights, consent, lawful processing, and anonymization.

3. PIPEDA (Canada)

  • Provides privacy protections for personal information in commercial activities across Canada.
  • Requires meaningful consent (express or implied depending on context), limits data use.
  • Mandates safeguards for personal information, including health data handled by businesses.

 

Privacy laws set the rules but generally do not prescribe exact tools or certifications—leaving room for frameworks to guide implementation.

 

 

Understanding Security Frameworks

Security frameworks provide practical controls and certification paths that IT teams can follow to demonstrate security readiness.

1. NIST AI RMF (USA)

  • Defines how to assess and manage AI risk.
  • Focuses on trustworthiness, transparency, and governance in AI systems.

2. ISO/IEC 27001 & 42001

  • 27001: Information security management system (ISMS).
  • 42001: World’s first AI management system standard (introduced 2023).

3. HITRUST CSF

  • A widely used certification that maps multiple regulations (HIPAA, GDPR, ISO, NIST).
  • Expanded its CSF in 2024 to address AI-related risks and began developing AI-specific assurance programs.

4. SOC 2

  • Third-party attestation report for security, availability, and confidentiality.
  • Commonly required by healthcare SaaS vendors to prove operational security.

Frameworks show the how—step-by-step, measurable, and certifiable methods for protecting data.

Key Differences IT Teams Must Understand

 

Aspect Privacy Laws (HIPAA, GDPR, PIPEDA) Security Frameworks (NIST, ISO, HITRUST)
Purpose Define legal rights & obligations Provide practical controls & processes
Scope Mandatory for covered entities Voluntary, but often expected by partners
Enforcement Enforced by regulators (e.g., HHS, EU DPAs) Enforced by auditors/certification bodies
Flexibility High-level requirements Specific technical and procedural controls
Proof Policies, consent records, contracts Certification reports, audits, test results

 

Practical Steps for IT Teams

 

Map laws to frameworks

  • Identify which laws apply to your AI product (e.g., HIPAA + GDPR).
  • Map each law’s obligations to framework controls (e.g., GDPR data minimization → ISO 27001 access control).

Adopt a framework as your baseline

  • Start with ISO 27001 or SOC 2 for general security.
  • Add NIST AI RMF or ISO 42001 for AI-specific risks.
  • Consider HITRUST CSF if you need multi-framework alignment.

Align privacy teams and IT teams

  • Ensure legal/compliance teams define what must be done, and IT/security teams implement how it gets done.
  • Regular cross-team reviews prevent gaps.

Prepare for dual expectations

  • Regulators will look for legal compliance (HIPAA, GDPR).
  • Investors, enterprise clients, and auditors will look for frameworks and certifications.
  • Both are needed to build long-term trust.

 

 

 

Key Takeaways

  • Privacy laws and security frameworks are complementary, not interchangeable.

  • Laws define mandatory obligations, while frameworks provide practical controls and certifications.

  • IT teams should treat compliance as a two-layer strategy: follow the law, then choose frameworks that demonstrate and operationalize compliance.

  • Staying proactive with frameworks like ISO 42001 or HITRUST’s AI certification can future-proof healthcare AI systems against new regulatory expectations.

Relevant Resources

HHS.gov – HIPAA Security Rule Summary: https://www.hhs.gov/hipaa/for-professionals/security/laws-regulations/index.html

European Commission – GDPR Overview: https://commission.europa.eu/law/law-topic/data-protection_en

NIST AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework

ISO/IEC 42001 – AI Management Standard: https://www.iso.org/standard/81230.html

HITRUST AI Security Certification: https://hitrustalliance.net/ai-risk-management-assurance-program/